{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "EjzT9h2bprZy"
   },
   "outputs": [],
   "source": [
    "import numpy\n",
    "import sklearn\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "4Tfck9VkxGq3"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda\\envs\\pytorch\\lib\\site-packages\\sklearn\\utils\\deprecation.py:143: FutureWarning: The sklearn.linear_model.logistic module is  deprecated in version 0.22 and will be removed in version 0.24. The corresponding classes / functions should instead be imported from sklearn.linear_model. Anything that cannot be imported from sklearn.linear_model is now part of the private API.\n",
      "  warnings.warn(message, FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.linear_model.logistic import LogisticRegression\n",
    "from sklearn.datasets import make_classification\n",
    "from sklearn.model_selection import KFold\n",
    "from sklearn.model_selection import LeaveOneOut\n",
    "from sklearn.model_selection import cross_val_predict\n",
    "from sklearn import metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "id": "MUC-cSN-zHSj"
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split,cross_val_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "id": "Pel20IlsrdhX"
   },
   "outputs": [],
   "source": [
    "X, y = make_classification(n_samples=1000, n_features=20, n_informative=15, n_redundant=5, random_state=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "id": "Xt7OBtwIxrv4"
   },
   "outputs": [],
   "source": [
    "model = LogisticRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 33
    },
    "id": "r14TreZHx6sa",
    "outputId": "9c18b995-81c0-4cc1-abfb-5b1258fe6d51"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.868 (0.032)\n"
     ]
    }
   ],
   "source": [
    "cv = KFold(n_splits=10, random_state=1, shuffle=True)\n",
    "scores = cross_val_score(model, X, y, scoring='accuracy', cv=cv, n_jobs=-1)\n",
    "print('Accuracy: %.3f (%.3f)' % (numpy.mean(scores), numpy.std(scores)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "id": "p-iYgZCc5Yqd"
   },
   "outputs": [],
   "source": [
    "y_pred = cross_val_predict(model, X, y, cv=cv)\n",
    "fpr, tpr, thresholds = metrics.roc_curve(y, y_pred)\n",
    "AUC = metrics.auc(fpr, tpr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 295
    },
    "id": "GMiMWja_9xwI",
    "outputId": "0459512d-2291-4772-a36d-c11b376c7e6f"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(fpr, tpr)\n",
    "plt.title('ROC curve for diabetes classifier')\n",
    "plt.xlabel('False Positive Rate (1 - Specificity)')\n",
    "plt.ylabel('True Positive Rate (Sensitivity)')\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 33
    },
    "id": "zksP4k9d-7JW",
    "outputId": "5f96cae5-c497-4a6d-a7b5-f53c6f70bb7f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.864\n"
     ]
    }
   ],
   "source": [
    "cv2 = LeaveOneOut()\n",
    "accuracy1 = 0\n",
    "for train,test in cv2.split(X):\n",
    "  model.fit(X[train],y[train])\n",
    "  y_p = model.predict(X[test])\n",
    "  if y_p == y[test]:accuracy1 += 1\n",
    "print(accuracy1 / numpy.shape(X)[0])\n",
    "# scores1 = cross_val_score(model, X, y, scoring='accuracy', cv=cv2, n_jobs=-1)\n",
    "# print('Accuracy: %.3f (%.3f)' % (numpy.mean(scores1), numpy.std(scores1)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "id": "N2CV_EY8LC7H"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.864 (0.343)\n"
     ]
    }
   ],
   "source": [
    "cv2 = LeaveOneOut()\n",
    "\n",
    "scores1 = cross_val_score(model, X, y, scoring='accuracy', cv=cv2, n_jobs=-1)\n",
    "print('Accuracy: %.3f (%.3f)' % (numpy.mean(scores1), numpy.std(scores1)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "id": "V3y6Pefv-qbR"
   },
   "outputs": [],
   "source": [
    "y_pred1 = cross_val_predict(model, X, y, cv=cv2)\n",
    "fpr1, tpr1, thresholds1 = metrics.roc_curve(y, y_pred1)\n",
    "AUC1 = metrics.auc(fpr1, tpr1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 295
    },
    "id": "V1eJ3oJPAtPC",
    "outputId": "8bb73c1a-b302-41e9-b9cf-55db2decc77f"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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f2dFzZPJlp1Bc07qxeBPJhvoapk6oL9gHvDeRdK78PCPIoFwTydc6eYs1V8FaqInkwAFi7IghjK/LNZEctUvrmdyd/JgRQ7yJpHMVwjOCKmFmoRfJvOaQjz3fzl2vPtFRRLOySBPJ8XWhS4JcE8nkIB/j4sXem0g6V308I+jnck0kX8u7W+8oh++iieTgAbD3utU01NUyZe863nDIuI5Btne+zepNJJ3LMs8I+qEXWzbwb3cuYPmaTaxY386WAk0khw8Z2NEt8JETR+7WB3xuNKfHmx5i5syZZUiFc65SFM0IJO0LXAK8HtgH2AQ8DfwG+K2Z7X6Fcnvs7gWvMHdxK+cfu88uzSJzfcCP70Yvkt5O3jnXlU6vJpK+B0wE7gauB1YAtcChwNnApyVdY2YP9kWgWdLU3MKUvev4xiXHlTsU51wGFLutvMHMni6w/GngF5KGAPuXJqzs2rJtB/OWrObiE/crdyjOuYzotH1fLhOQ9FZJu21nZlvMbGEpg8uiBcvWsGnrdk4+cK9yh+Kcy4g0Db0vBv4m6cuSppQ6oKxram4BYPrkMWWOxDmXFV1mBGZ2GXAcsAiYJWmOpCsl1ZU8ugxqam5lyt517DV8SLlDcc5lRKpXP81sHXAncDswAXgb8LikD5UwtszZsm0H815s5eQD/WnAOdd3uswIJJ0v6S6gERgMTDeztwDHAB8vbXjZ8uSyNWzeusMzAudcn0rTGP0fgK/lNxM1s42SrihNWNnUtCjUD5w02SuKnXN9J03R0Kv5mYCk6wHM7I8liSqjmhaH9wdGe/2Ac64PpckIziqw7C29HUjWtW/bzmMvruaUg7xYyDnXtzrNCCS9X9JTwBRJCxKfxcCCNAeXdLakFyQtlHRNgfX7S7pf0hPx2Of0PCmV7cmla71+wDlXFsXqCG4Dfgv8F5C8iK83s9auDixpIHAT4YliGfCopNlm9mxis88APzWz/5M0FbgHmNS9JFSHpuYWJK8fcM71vWJFQ2ZmS4APAusTHySluVpNBxaaWbOZbSE0PT0//xxAfZweCbycPvTqEvoXqmfUMK8fcM71LVn+GIO5FdLdZnZuLAoyINmNpZnZgUUPLF0InG1m/xTn3wWcZGZXJbaZAPweGA0MB840s8cKHOtK4EqAhoaGE26//fZuJHGntrY2RowY0aN9S2nrDuMDf9jIzP0G8Y7Da3r12P01zaXkac4GT3P3zJw58zEzm1ZoXadFQ2Z2bvw7uUdnTedSYJaZ3SDpFOCHko7M797azG4BbgGYNm2azZgxo0cna2xspKf7ltIji1vZumMOF77hGGYcsXevHru/prmUPM3Z4GnuPWleKJst6VJJw7p57OVAsgvNfeOypCuAnwKY2RxCN9dju3meijdnUagfmO71A865MkjTfPQGwsA0z0m6U9KFkmpT7PcocIikybHL6kuA2XnbvAScASDpcEJGsDJ19FWiqbmFw71+wDlXJmk6nXvAzD4AHAjcDFxEGKSmq/22AVcB9wLPEVoHPSPpOknnxc0+DrxP0pPAT4DLrbNKiyq1eet2Hn9ptTcbdc6VTarxDiUNBd5K6JL6eOD7afYzs3sITUKTy65NTD8LnJo22Gr05NI1tG/b4S+SOefKpsuMQNJPCU1Bfwf8D/CAj1Xce+bE9wemT/L6AedceaR5IvgucKmZbS91MFnU1NzC1An1jBw2uNyhOOcyqtjg9aeb2Z8I7fvPl7TLejP7RYljq3qhfmAN7zr5gHKH4pzLsGJPBG8E/kSoG8hngGcEe2j+0jVs2baDU7yi2DlXRsVeKPtsnLzOzBYn10kq5UtmmZHrX+hEf3/AOVdGad4j+HmBZXf2diBZNGdRC0fsU8/IoV4/4Jwrn2J1BFOAI4CRkv4hsaqe8OKX2wObt27niaVreLfXDzjnyqxYHcFhwLnAKHatJ1gPvK+EMWXCEy+F+gF/kcw5V27F6gh+BfxK0imxHyDXi5qaWxjg9QPOuX6gWNHQv5nZl4F3SLo0f72ZfbikkVW5Oc0tHLHPSK8fcM6VXbGioefi33l9EUiWbN66nfkvreE9r/P6Aedc+RUrGvp1/NvRr5CkAcAIM1vXB7FVrcdfWs2W7V4/4JzrH9KMR3CbpHpJw4GngWclXV360KpXU3Or1w845/qNNO8RTI1PAH9PGMx+MvCuUgZV7ZoWtXDkxJHU13r9gHOu/NJkBIMlDSZkBLPNbCuhiwnXA5u2bGf+0jVeLOSc6zfSZAQ3A0sInc89KOkAwOsIeuiJjvoBLxZyzvUPXXZDbWY3AjcmFr0oaWbpQqpuHe8P+PgDzrl+Is3ANDXABcCkvO2vK1FMVa2puZWjJo6kzusHnHP9RJqioV8B5wPbgA2Jj+umTVu288RSH5/YOde/pBmhbF8zO7vkkWTA4y+tZut284zAOdevpHki+Iuko0oeSQY0NbcwcICYNml0uUNxzrkOaZ4ITgMul7QYaAcEmJkdXdLIqlBTc3h/wOsHnHP9SZqM4C0ljyIDcu8PvPc0H9zNOde/dFk0ZGYvAvsBp8fpjWn2c7t67EWvH3DO9U9p+hr6LPBJ4N/josHAj0oZVDXK1Q/4+wPOuf4mzZ3924DziE1GzexloK6UQVWjpuYWjpo4khE1aUrjnHOu76TJCLaYmRH7F4q9kLpu2LhlG08u8/6FnHP9U5qM4KeSbgZGSXof8Afg26UNq7rsrB/wYiHnXP+Tpq+h/5Z0FqGjuUOBa83svpJHVkV2vj/gGYFzrv9JVWBtZvdJehx4A9Ba2pCqT65/Ia8fcM71R50WDUm6W9KRcXoCYXSy9wI/lPSRNAeXdLakFyQtlHRNJ9tcJOlZSc9Iuq37SejfNm7ZxpNL13DKQV4/4Jzrn4rdok42s6fj9D8C95nZuyXVAQ8DXy92YEkDgZuAs4BlwKOSZpvZs4ltDiE0Sz3VzFZLGt/zpPRP85asZtsOf3/AOdd/Fass3pqYPgO4B8DM1gM7Uhx7OrDQzJrNbAtwO6EX06T3ATeZ2ep47BVpA68UHfUDB3j/Qs65/qnYE8FSSR8i3M0fD/wOQNJQwktlXZkILE3MLwNOytvm0HjMh4GBwOfM7Hf5B5J0JXAlQENDA42NjSlOv7u2trYe79tTv5+/iUl14tE5D/XpeXPKkeZy8zRng6e59xTLCK4gDD5zJnCxma2Jy08GvteL5z8EmAHsSxgK86jEuQAws1uAWwCmTZtmM2bM6NHJGhsb6em+PbGhfRtLfv97rnzDgcyYMaXPzpvU12nuDzzN2eBp7j2dZgSxmOZfCiy/H7g/xbGXE/ooytk3LktaBsw1s63AYkl/JWQMj6Y4fr8370WvH3DO9X/FWg19O9dqqMC64ZLeK+mdRY79KHCIpMmShgCXALPztvkl4WkASWMJRUXN6cPv35qaWxg0QJzg9QPOuX6sWNHQTcC1cVCap4GVQC3hjr0euBX4cWc7m9k2SVcB9xLK/281s2ckXQfMM7PZcd2bJD0LbAeuNrOWXkhXv9DU3MLR+45kuL8/4Jzrx4oVDc0HLpI0ApgGTAA2Ac+Z2QtpDm5m9xBbGyWWXZuYNuBj8VNVNrRvY8GytfzzGw4sdyjOOVdUmi4m2oDG0odSXea9uJrtO8xfJHPO9Xs+wEyJzFnk9QPOucrgGUGJNDW3cMx+oxg2xOsHnHP9W+qMQNKwUgZSTdrat/HU8rXe7bRzriKkGarydbFVz/Nx/hhJ/1vyyCrYvCWtoX7gwLHlDsU557qU5onga8CbgRYAM3uS0B2168Sc5hYGDxTHHzCq3KE451yXUhUNmdnSvEXbSxBL1WhqbuWYfb1+wDlXGdJkBEslvQ4wSYMlfQJ4rsRxVaz1m7fy9PK13q2Ec65ipMkI/gX4IKE30eXAscAHShhTRfP3B5xzlSZN2cVhZrZLn0KSTiUMTuPyNOXqB/b39wecc5UhzRPBN1Muc0DTohaO3W8UQ4cMLHcozjmXSqdPBJJOAV4HjJOU7AuontCJnMuzfvNWnlq+lg/OPLjcoTjnXGrFioaGACPiNnWJ5euAC0sZVKWat2Q1OwyvKHbOVZRivY8+ADwgaZaZvdiHMVWspuYWhgwc4PUDzrmKkqayeKOkrwBHEMYjAMDMTi9ZVBVqTrPXDzjnKk+ayuIfE7qXmAz8B7CEKhlKsjet63h/wPsXcs5VljQZwRgz+y6w1cweMLP3Av40kGfeklavH3DOVaQ0RUNb499XJP0d8DLgt715mppbQ/2Ajz/gnKswaTKCL0gaCXyc8P5APfCRUgZVieYsauHY/UdRO9jrB5xzlaXLoiEzu9vM1prZ02Y208xOAFr7ILaKsXbTVp552fsXcs5VpmIvlA0ELiL0MfQ7M3ta0rnAp4ChwHF9E2L/t7N+wEvMnHOVp1jR0HeB/YBHgBslvQxMA64xs1/2QWwVw98fcM5VsmIZwTTgaDPbIakWeBU4yMxa+ia0ytHU3MpxXj/gnKtQxeoItpjZDgAz2ww0eyawO68fcM5VumJPBFMkLYjTAg6K8wLMzI4ueXQV4NHF/v6Ac66yFcsIDu+zKCpYU3MLQwYN4Lj9R5U7FOec65Finc55R3MpNC1u4XivH3DOVbBUg9e7wtZu3MozL6/zYiHnXEXzjGAPPLKkFfP6AedchUuVEUgaKumwUgdTaXL1A8fuN6rcoTjnXI91mRFIeiswH/hdnD9W0uwSx1URmppbOGH/0V4/4JyraGmeCD4HTAfWAJjZfMLYBF2SdLakFyQtlHRNke0ukGSSpqU5bn+wduNWnn3F6wecc5UvTUaw1czW5i2zrnaKfRXdBLwFmApcKmlqge3qgH8F5qaIpd+Yu7gl1g94/0LOucqWJiN4RtI7gIGSDpH0TeAvKfabDiw0s2Yz2wLcDpxfYLvPA9cDm9MG3R80NbdSM2gAx3j9gHOuwqUZj+BDwKeBduA24F7gCyn2mwgsTcwvA05KbiDpeGA/M/uNpKs7O5CkK4ErARoaGmhsbExx+t21tbX1eN98f1iwiQProenhP/fK8UqlN9NcKTzN2eBp7kVmVvQDHN/VNp3sdyHwncT8u4D/ScwPABqBSXG+EZjW1XFPOOEE66n777+/x/smrd7QbpOuudu+8Ye/9srxSqm30lxJPM3Z4GnuHmCedXJdTVM0dIOk5yR9XtKR3chjlhO6sc7ZNy7LqQOOBBolLQFOBmZXQoXx3MX+/oBzrnqkGaFsJjATWAncLOkpSZ9JcexHgUMkTZY0BLgE6Gh2amHUs7FmNsnMJgFNwHlmNq8nCelLTc0tsX5gZLlDcc65PZbqhTIze9XMbgT+hfBOwbUp9tkGXEWoU3gO+KmZPSPpOknn9Tzk8mtqbuWEA0ZTM8jfH3DOVb4uK4slHQ5cDFwAtAB3EAay75KZ3QPck7esYCZiZjPSHLPc1mzcwvOvruNjZx5a7lCcc65XpGk1dCvh4v9mM3u5xPH0e03NsX7gIK8fcM5Vhy4zAjM7pS8CqRRNzS3UDh7A0ft6/YBzrjp0mhFI+qmZXSTpKXZ9kzjTI5Q1Nbd4/YBzrqoUeyL41/j33L4IpBKs3rCF519dz8fP8voB51z16LTVkJm9Eic/YGYvJj/AB/omvP5l7uJWAE7x+gHnXBVJ03z0rALL3tLbgVSCnfUDo8odinPO9ZpidQTvJ9z5HyhpQWJVHfBwqQPrj5qaW5h2wF4MGeQDuznnqkexOoLbgN8C/wUkxxJYb2atJY2qH2qN9QOfeNOEcofinHO9qlhGYGa2RNIH81dI2itrmcEji1sArx9wzlWfrp4IzgUeIzQfVWKdAQeWMK5+Z86iFoYOHshRE0eVOxTnnOtVnWYEZnZu/JtqWMpq19TcyrRJo71+wDlXddIMXn+qpOFx+jJJX5W0f+lD6z9a2tp54bX13u20c64qpbm9/T9go6RjCJ3NLQJ+WNKo+plH4vsDPj6xc64apckItsXRbc4njDB2E6EJaWY0NYf6AX9/wDlXjdL0Prpe0r8Thpp8vaQBwODShtW/zGluYdqk0Qwe6PUDzrnqk+bKdjFh4Pr3mtmrhCEnv1LSqPqRVW3t/PW1Nq8fcM5VrTRDVb4K/BgYKelcYLOZ/aDkkfUTO+sHPCNwzlWnNK2GLgIeAd4OXATMlXRhqQPrL5qaWxg2ZKCPP+Ccq1pp6gg+DZxoZisAJI0D/gDcWcrA+os5i1qYNmkvrx9wzlWtNFe3AblMIGpJuV/FW9XWzt9WtHmzUedcVUvzRPA7SfcCP4nzF5M3IH21mtvs9QPOueqXZsziqyX9A3BaXHSLmd1V2rD6h6bmFoYPGchRE71+wDlXvYqNR3AI8N/AQcBTwCfMbHlfBdYfhPcHvH7AOVfdil3hbgXuBi4g9ED6zT6JqJ9Yub6dhSv8/QHnXPUrVjRUZ2bfjtMvSHq8LwLqL+bG8Qe8otg5V+2KZQS1ko5j5zgEQ5PzZlbVGUOufuBIrx9wzlW5YhnBK8BXE/OvJuYNOL1UQfUHTc2tnDjZ6wecc9Wv2MA0M/sykP5kxfrNLFzRxoUn7FvuUJxzruT8drcAf3/AOZclnhEU0NTcwoiaQRy5T325Q3HOuZIraUYg6WxJL0haKOmaAus/JulZSQsk/VHSAaWMJ62m5hZOnDSaQV4/4JzLgDS9jyqOVXxtnN9f0vQU+w0EbgLeAkwFLpU0NW+zJ4BpZnY0oRO7L3c3Ab1txbrNLFq5wYuFnHOZkeaW93+BU4BL4/x6wgW+K9OBhWbWbGZbgNsJw112MLP7zWxjnG0iDHpTVk0+/oBzLmPSdDp3kpkdL+kJADNbLWlIiv0mAksT88uAk4psfwXw20IrJF0JXAnQ0NBAY2NjitPvrq2trct9f/FMO7UDYdXfnqBxkYpuWwnSpLnaeJqzwdPce9JkBFtjMY9Bx3gEO3ozCEmXAdOANxZab2a3ALcATJs2zWbMmNGj8zQ2NtLVvtc91sgpB4/mjNO7LP2qCGnSXG08zdngae49aYqGbgTuAsZL+k/gIeCLKfZbDuyXmN83LtuFpDMJg9+cZ2btKY5bMivWbaZ55QZOOciLhZxz2ZGmG+ofS3oMOIPQvcTfm9lzKY79KHCIpMmEDOAS4B3JDWKXFTcDZ+cNflMWc5pz/Qt5RuCcy44uMwJJ+wMbgV8nl5nZS8X2M7Ntkq4C7gUGArea2TOSrgPmmdls4CvACOBnkgBeMrPzepyaPdTU3EpdzSCmTvD3B5xz2ZGmjuA3hPoBAbXAZOAF4IiudjSze8gbzczMrk1Mn9mdYEttbnMLJ07ey98fcM5lSpqioaOS85KOBz5QsojK5LV1m2letYFLp+9f7lCcc65PdfvWN3Y/XawZaEVq8voB51xGpakj+FhidgBwPPByySIqk6bmllA/4P0LOecyJk0dQV1iehuhzuDnpQmnfJqaW5k+eS8GDqj8l8icc647imYE8UWyOjP7RB/FUxavrt3M4lUbeOdJXj/gnMueTusIJA0ys+3AqX0YT1l4/YBzLsuKPRE8QqgPmC9pNvAzYENupZn9osSx9Zmm5hbqagdxuL8/4JzLoDR1BLVAC2GM4tz7BAZUVUZwktcPOOcyqlhGMD62GHqanRlAjpU0qj70ytpNLGnZyGUn94sxcZxzrs8VywgGErp/KHSbXDUZgY9P7JzLumIZwStmdl2fRVImcxa1UO/1A865DCv2ZnEmCsybFrcwffIYrx9wzmVWsYzgjD6LokxeXrOJF1s2cvKBe5U7FOecK5tOMwIza+3LQMph7uLw/oAPROOcy7JM97c8Z1ELI4cO5vC9vX7AOZddmc4Icv0LDfD6AedchmU2I1i+ZhMvtW70ZqPOuczLbEYwt6N/Ia8ods5lW2YzgqZmrx9wzjnIcEYwJ/Yv5PUDzrmsy2RGsGz1Rpa2bvL6AeecI6MZgfcv5JxzO2UyI2hqbmHUsMFM2buu642dc67KZTIj8PoB55zbKXMZwdLWjSxb7fUDzjmXk7mMYO5irx9wzrmkzGUETc0tjB42mMMavH7AOecggxnBnEUtnDR5jNcPOOdclKmMYOXGHSxfs8m7lXDOuYRMZQTPt24H4GQff8A55zpkLCPYwehhgzl0vNcPOOdcTkkzAklnS3pB0kJJ1xRYXyPpjrh+rqRJpYzn+dbtnHyg1w8451xSyTICSQOBm4C3AFOBSyVNzdvsCmC1mR0MfA24vlTxLG3dSMtm82ajzjmXp5RPBNOBhWbWbGZbgNuB8/O2OR/4fpy+EzhDUklu1+d0jD/gGYFzziUNKuGxJwJLE/PLgJM628bMtklaC4wBViU3knQlcCVAQ0MDjY2N3Q5m2WvbOHovY/lz83jl+ewUDbW1tfXo+6pknuZs8DT3nlJmBL3GzG4BbgGYNm2azZgxo9vHmAEc39hIT/atZI2e5kzwNGdDqdJcyqKh5cB+ifl947KC20gaBIwEWkoYk3POuTylzAgeBQ6RNFnSEOASYHbeNrOB98TpC4E/mZmVMCbnnHN5SlY0FMv8rwLuBQYCt5rZM5KuA+aZ2Wzgu8APJS0EWgmZhXPOuT5U0joCM7sHuCdv2bWJ6c3A20sZg3POueIy9Waxc8653XlG4JxzGecZgXPOZZxnBM45l3GqtNaaklYCL/Zw97HkvbWcAZ7mbPA0Z8OepPkAMxtXaEXFZQR7QtI8M5tW7jj6kqc5GzzN2VCqNHvRkHPOZZxnBM45l3FZywhuKXcAZeBpzgZPczaUJM2ZqiNwzjm3u6w9ETjnnMvjGYFzzmVcVWYEks6W9IKkhZKuKbC+RtIdcf1cSZPKEGavSpHmj0l6VtICSX+UdEA54uxNXaU5sd0FkkxSxTc1TJNmSRfF3/oZSbf1dYy9LcW/7f0l3S/pifjv+5xyxNlbJN0qaYWkpztZL0k3xu9jgaTj9/ikZlZVH0KX14uAA4EhwJPA1LxtPgB8K05fAtxR7rj7IM0zgWFx+v1ZSHPcrg54EGgCppU77j74nQ8BngBGx/nx5Y67D9J8C/D+OD0VWFLuuPcwzW8Ajgee7mT9OcBvAQEnA3P39JzV+EQwHVhoZs1mtgW4HTg/b5vzge/H6TuBMyRV8kDGXabZzO43s41xtokwYlwlS/M7A3weuB7Y3JfBlUiaNL8PuMnMVgOY2Yo+jrG3pUmzAfVxeiTwch/G1+vM7EHC+CydOR/4gQVNwChJE/bknNWYEUwElibml8VlBbcxs23AWmBMn0RXGmnSnHQF4Y6iknWZ5vjIvJ+Z/aYvAyuhNL/zocChkh6W1CTp7D6LrjTSpPlzwGWSlhHGP/lQ34RWNt39/96lihi83vUeSZcB04A3ljuWUpI0APgqcHmZQ+lrgwjFQzMIT30PSjrKzNaUM6gSuxSYZWY3SDqFMOrhkWa2o9yBVYpqfCJYDuyXmN83Liu4jaRBhMfJlj6JrjTSpBlJZwKfBs4zs/Y+iq1UukpzHXAk0ChpCaEsdXaFVxin+Z2XAbPNbKuZLQb+SsgYKlWaNF8B/BTAzOYAtYTO2apVqv/v3VGNGcGjwCGSJksaQqgMnp23zWzgPXH6QuBPFmthKlSXaZZ0HHAzIROo9HJj6CLNZrbWzMaa2SQzm0SoFznPzOaVJ9xekebf9i8JTwNIGksoKmruwxh7W5o0vwScASDpcEJGsLJPo+xbs4F3x9ZDJwNrzeyVPTlg1RUNmdk2SVcB9xJaHNxqZs9Iug6YZ2azge8SHh8XEiplLilfxHsuZZq/AowAfhbrxV8ys/PKFvQeSpnmqpIyzfcCb5L0LLAduNrMKvZpN2WaPw58W9JHCRXHl1fyjZ2knxAy87Gx3uOzwGAAM/sWoR7kHGAhsBH4xz0+ZwV/X84553pBNRYNOeec6wbPCJxzLuM8I3DOuYzzjMA55zLOMwLnnMs4zwgyTNJ2SfMTn0lFtm3rhfPNkrQ4nuvx+BZod4/xHUlT4/Sn8tb9ZU9jjMfJfS9PS/q1pFFdbH9sT3q8lDRB0t1xekzsQbNN0v/0MO5Pxx5HF8T4T+rJcYoc/57cdyHpw5Kek/RjSecV6/01bv+X+HeSpHekONe5sYmo6wPefDTDJLWZ2Yje3rbIMWYBd5vZnZLeBPy3mR29B8fb45i6Oq6k7wN/NbP/LLL95YSeTa/q5nm+AjxkZr+SNBw4jvA29JE9ONYphC41ZphZe3yZbIiZlaQDNknPA2ea2bJu7jcD+ISZndvFdgIeB05NdJboSsSfCFwHSSMUxip4XNJTknbrzTPexT6YuGN+fVz+Jklz4r4/k9TVBfpB4OC478fisZ6W9JG4bLik30h6Mi6/OC5vlDRN0peAoTGOH8d1bfHv7ZL+LhHzLEkXShoo6SuSHo13zf+c4muZQ+zQS9L0mMYnJP1F0mEKb7teB1wcY7k4xn6rpEfitoV6RQW4APgdgJltMLOH6HkvqROAVbmuQ8xsVS4TkLRE0pfjb/qIpNz3Pk7Sz+P38aikU+PyEZK+F7dfIOmCxHHGSvoWoVvo30r6qKTLc08xkhok3RV/tyclvS4uzz1Rfgl4ffyuPhr/LR2bS4SkhyQdE18IawSKZhiul5SyX23/9O8P4c3T+fFzF+FN8/q4bizhzcXcU2Nb/Ptx4NNxeiChT5+xhAv78Lj8k8C1Bc43C7gwTr8dmAucADwFDCe8+fwM4c74AuDbiX1Hxr+NxHEFcjEltsnF+Dbg+3F6CKGnxqHAlcBn4vIaYB4wuUCcbYn0/Qw4O87XA4Pi9JnAz+P05cD/JPb/InBZnB5F6O9neN45JgOPFTj3Lsfqxm85Iv6OfwX+F3hjYt2SxG/2bsJTGcBtwGlxen/guTh9PfD1xP6jE8cZW2C6I2bgDuAjie8v97vlvtMZufPH+ffkzkXoDmNeYt07gW+W+/9JFj5V18WE65ZNZnZsbkbSYOCLkt4A7CDcCTcAryb2eRS4NW77SzObL+mNhAFBHg5P9Awh3EkX8hVJnyH0BXMFoY+Yu8xsQ4zhF8DrCXfKN0i6nnDh+HM30vVb4BuSaoCzgQfNbFMsjjpa0oVxu5GEDtkW5+0/VNL8mP7ngPsS239f0iGErgwGd3L+NwHnSfpEnK8lXmgT20ygF/vDMbM2SScQvruZwB2SrjGzWXGTnyT+fi1OnwlM1c6hOOrjk9yZJLpdsTi2QUqnEzIbzGw7oYv3Yn4G/D9JVwPvJdws5KwA9unGuV0PeUbgkt4JjANOMLOtCr121iY3MLMHY0bxd8AsSV8FVgP3mdmlKc5xtZndmZuRdEahjczsrwrjCZwDfEHSH80sVeWhmW2W1Ai8GbiYMJgJhBGdPmRm93ZxiE1mdqykYYQ+bj4I3EgY5OZ+M3ubQsV6Yyf7C7jAzF4odg7yvtuuKFT+3hxnr7W8/pTihbeR0OPqU4S77Vm51clN498BwMlmtktxlPpwjCYz2yjpPsJgKxcRnhBzagnfkysxryNwSSOBFTETmAnsNq6xwljHr5nZt4HvEIbUawJOTZQ9D5d0aMpz/hn4e0nDFCpM3wb8WdI+wEYz+xGhw7xC47JujU8mhdxB6Iwr93QB4aL+/tw+kg6N5yzIQiXlh4GPa2d35bnufi9PbLqeUESWcy/wIcUrqkLPr/n+Ckzq7NydxDPXzI6Nn/zeZQ+LTyo5xwIvJuYvTvzNPa39nsQgLomy+vsImV9u+ehuhPlHwlCoxDqZkXnr878rCP+ObgQezXv6OBQoOG6v612eEbikHwPT4t3ku4HnC2wzA3hS0hOEi8o3zGwl4cL4E0kLCBeaKWlOaGaPE+5aHyHUGXzHzJ4AjgIeiUU0nwW+UGD3W4AFucriPL8nDL7zBwtDHEK44DwLPK4wMPjNdPFUHGNZQBj85MvAf8W0J/e7n1DEMl+hUvvzhGKjBZKeifP5x90ALMplnhAqY4mD6UhapthMNqURhGKrZ+NvMJUwclfO6Lj8X4GPxmUfJvzeCxR6K/2XuPwLcfunJT1JKGpK61+BmfHf0GMxjqQFwPZYkfxRADN7DFgHfC9v25lAtYwu169581HnykTS2wjFcJ8p8XmWECrYV5XyPD0Vn/4agSkWRxWT1ADcZmYFiw5d7/InAufKxMzuIrS+ySxJ7yY8CX7adh1acn9CCzXXB/yJwDnnMs6fCJxzLuM8I3DOuYzzjMA55zLOMwLnnMs4zwiccy7j/j8eLlMhksO+xAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(fpr1, tpr1)\n",
    "plt.title('ROC curve for diabetes classifier')\n",
    "plt.xlabel('False Positive Rate (1 - Specificity)')\n",
    "plt.ylabel('True Positive Rate (Sensitivity)')\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "name": "Untitled0.ipynb",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
